Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers

INTRODUCTION: Immunotherapy is regarded one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds - urging the quest for predictive biomarkers. We hypothesize that Artificial Intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as non-invasive radiomic biomarkers for immunotherapy response. PATIENTS AND METHODS: In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We performed a AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a non-invasive machine learning biomarker capable of distinguishing between immunotherapy responding and non-responding. To define the biological basis of the radiographic biomarker, we performed gene-set enrichment analysis in an independent dataset of 262 NSCLC patients. RESULTS: The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, p < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, p = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (p < 0.001), resulting in a one year survival difference of 24% (p = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy. CONCLUSIONS: These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as non-invasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.